Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite

He, Qianyu; Wang, Zhenzhan; Li, Jiaoyang

doi:10.3390/s21144673

Cited by 15 publications

(4 citation statements)

References 38 publications

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“…On the other hand, a change in the line conductivity will lead to a change in impedance, which will cause a change in the reflection coefficient, as in Equation (3).…”

Section: Sensing Principlementioning

confidence: 99%

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Selective Outdoor Humidity Monitoring Using Epoxybutane Polyethyleneimine in a Flexible Microwave Sensor

et al. 2022

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The rise of gas-sensing applications and markets has led to microwave sensors associated to polymer-based sensitive materials gaining a lot of attention, as they offer the possibility to target a large variety of gases (as polymers can be easily functionalised) at ultra-low power and wirelessly (which is a major concern in the Internet of Things). A two-channel microstrip sensor with one resonator coated with 1,2 epoxybutane-functionalised poly(ethyleneimine) (EB-PEI) and the other left bare was designed and fabricated for humidity sensing. The sensor, characterised under controlled laboratory conditions, showed exponential response to RH between 0 and 100%, which is approximated to −1.88 MHz/RH% (−0.03 dB/RH%) and −8.24 MHz/RH% (−0.171 dB/RH%) in the RH ranges of 30–80% and 80–100%, respectively. This is the first reported use of EB-PEI for humidity sensing, and performances, especially at high humidity level (RH > 80%), as compared with transducer working frequencies, are better than the state of the art. When further tested in real outdoor conditions, the sensor shows satisfying performances, with 4.2 %RH mean absolute error. Most importantly, we demonstrate that the sensor is selective to relative humidity alone, irrespective of the other environmental variables acquired during the campaign (O3, NO, NO2, CO, CO2, and Temperature). The sensitivities obtained outdoors in the ranges of 50–70% and 70–100% RH (−0.61 MHz/%RH and −3.68 MHz/%RH, respectively) were close to lab results (−0.95 MHz/%RH and −3.51 MHz/%RH, respectively).

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“…On the other hand, a change in the line conductivity will lead to a change in impedance, which will cause a change in the reflection coefficient, as in Equation (3).…”

Section: Sensing Principlementioning

confidence: 99%

“…Environmental, atmospheric and air quality sensors have attracted a lot of attention in recent years [1][2][3][4]. Notably, relative humidity (RH) sensors have been receiving significant attention.…”

Section: Introductionmentioning

confidence: 99%

Selective Outdoor Humidity Monitoring Using Epoxybutane Polyethyleneimine in a Flexible Microwave Sensor

et al. 2022

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“…Meteorological satellite remote sensing can make up for the shortcomings of conventional observation. Additionally, satellite-borne hyperspectral infrared remote sensing technology is expected to achieve high spatial and temporal resolution and high precision detection of atmospheric temperature and humidity field [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/VASS in the Arctic Region Using Neural Networks

Petropoulos

et al. 2023

Remote Sensing

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In this study, a new technique is proposed to retrieve temperature and relative humidity profiles under clear sky conditions in the Arctic region based on the artificial neural network (ANN) algorithm using Fengyun-3D (FY-3D) vertical atmospheric sounder suit (VASS: HIRAS, MWTS-II, and MWHS-II) observations. This technology combines infrared (IR) and microwave (MW) observations to improve retrieval accuracy in the middle and low troposphere by reducing the sensitivity of the neural networks (NNs) to cloud coverage. The approach was compared against other methods available in the literature on retrieving profiles only from FY-3D/HIRAS data. Furthermore, its retrieval performance was tested by comparing the NNs’ prediction accuracy versus the corresponding FY-3D/VASS and Aqua/AIRS L2 products. The results showed that: (1) NNs retrieval accuracy is higher during the warm season and over the ocean; (2) the retrieval accuracy of NNs has been significantly improved compared with satellite L2 products; (3) referring to radiosonde observations, the retrieval accuracy of NNs below 600 hPa is effectively improved by adding the information of the MW channel, especially on land where cloud clearing is more difficult. The root mean square error (RMSE) of temperature and relative humidity in the cold season were reduced by 0.3 K and 2%, respectively. The advanced NNs proposed herein offer a more stable retrieval performance compared with NNs built only by FY-3D/HIRAS data. The study results indicated the potential value in time and space domain of the NN algorithm in retrieving temperature and relative humidity profiles of the Arctic region from FY-3D/VASS observations under clear-sky conditions. All in all, this work enhances our knowledge towards improving operational use of FY-3D satellite data in the Arctic region.

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“…Zhang et al (2019) used a backward propagation neural network to retrieve the sea level pressure in the TC inner-core regions, which has a root mean square error of less than 4 hPa when compared with the FNL analysis. The root mean square errors of the humidity profiles retrieved using a deep neural network and the ERA-Interim are less than 20%, with a maximum value of 18% at about 850 hPa (He et al, 2021). However, except for satellite data assimilation, there were very few studies that make a direct use of microwave TB distributions to studying structures of TCs.…”

mentioning

confidence: 99%

Comparison of Cloud/Rain Band Structures of Typhoon Muifa (2022) Revealed in FY‐3E MWHS‐2 Observations With All‐Sky Simulations

Bi,

Zou

2023

JGR Atmospheres

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All‐sky simulations of brightness temperature (TB) at Microwave Humidity Sounder‐2 (MWHS‐2) channel 15 from Fengyun‐3E (FY‐3E) are generated based on the European Center for Medium‐Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis for four typhoon cases. For Typhoon Muifa (2022), the MWHS‐2 TB observations have a strongest symmetric component within about 210‐km radial distances. Although much weaker, the symmetric component in the GFS all‐sky simulations compares more favorably with that of observations than the ERA5 all‐sky simulations. Due to ice scattering effects at 183.31 ± 7 GHz, spatial distribution patterns of TB observations and all‐sky simulations are quite similar to those of ice water path. The more cloud ice, the lower the TB. Deep convective systems within Muifa are captured by TB observations but not by simulations. Using an objective azimuthal‐spectral‐analysis based center‐fixing algorithm, the center positions of Typhoon Muifa can be well determined from both MWHS‐2 observations and GFS all‐sky simulations, but not from the ERA5 all‐sky TB simulations. Compared with the best track, the average center position errors for Muifa are 16.2, 24.9 and 74.4 km based on FY‐3E MWHS‐2 TB observations, the GFS all‐sky TB simulations, and the ERA5 all‐sky simulations, respectively. The conclusions of TC cloud/rain band structures and center positions of all‐sky simulations using the NCEP‐GFS analysis being better matching with those of FY‐3E MWHS‐2 observations than the ERA5 all‐sky simulations for Typhoon Muifa also hold true for three other typhoons.

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Application of the Deep Neural Network in Retrieving the Atmospheric Temperature and Humidity Profiles from the Microwave Humidity and Temperature Sounder Onboard the Feng-Yun-3 Satellite

Cited by 15 publications

References 38 publications

Selective Outdoor Humidity Monitoring Using Epoxybutane Polyethyleneimine in a Flexible Microwave Sensor

Selective Outdoor Humidity Monitoring Using Epoxybutane Polyethyleneimine in a Flexible Microwave Sensor

Temperature and Relative Humidity Profile Retrieval from Fengyun-3D/VASS in the Arctic Region Using Neural Networks

Comparison of Cloud/Rain Band Structures of Typhoon Muifa (2022) Revealed in FY‐3E MWHS‐2 Observations With All‐Sky Simulations

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